Research Questions

What is the most frequently mentioned country in Jeopardy?

Is there a correlation between land area and mentions in Jeopardy?

What is the frequency of mentions in DailyDouble for each country?

Introduction

Methods

• Read in Jeopardy question data compiled by Github user jwolle1; initially (at all?) used Season 1

• Used read_csv from the readr package, although we manully downloaded the full dataset, a zipped file.

• The raw data already conformed to tidy data conventions, so no special pre-processing was needed on this front.

Next Steps . . .

• We needed a list of names associated with countries

• Our basis was the CountrySynomnyms dataframe from the rworldmap package, this included up to 8 synonymous names for every country recognized as of 2005 (as well as historical country names), along with 3-letter abbreviations following the ISO3 standard.

• We used the pivot_longer function from tidyr to convert this dataframe to two columns: ISO3 names and names (NAs were removed).

Next Steps Continued . . .

• To expand this dataset, names from the “lengthend” country names dataframe were matched against a list of country adjectives and demonyms (e.g. “Russian”, “Russians”) scraped from the Wikipedia page using the rvest package.

• These additional names were also converted to a single column, and matched to ISO3 codes.

Matching Names

• We then used the str_detect and str_extract_all functions from stringr in tandem to locate and extract matches in Jeopardy questions or answers.

• We avoided some false positives (e.g. “Indiana” includes the string “India”) by excluding any match that was follwed by a letter (our target list included both singualr and plural forms of country adjectives/demonyms).

Finally . . .

• Because str_extract_all generates a list, we used to add NA values to equialize the products’ lengths, and unnest from <?> to convert these into separate rows.

• We added the source (question or answer) as a metadata column, and merged both derived datasets to get a total frequency.

Mapping:

• The resulting table, with the original jeopardy data, our fequency tallies, and the ISO3 codes, was matched to a world map <~LowRest~ or something> bundled with rworldmap, which also included ISO3 codes.

• From there, were were able to use the leaflet package to make an interactive world mapwith frequncy of references in jeopardy color-coded with the scheme we end up using in <ggplot?>

What is the most frequently mentioned country in Jeopardy?

• We used leaflet package to map out out the countries mentioned, how many times they were mentiones and the population of each country.

## Is there a correlation between land area and mentions in Jeopardy?

## What is the frequency of mentions in DailyDouble for each country?

Results

• France is the most mentioned country coming in at 1,070 mentions throughout all the seasons.

Conclusion

Tutorial

Data Manipulation

Leaflet Package

library(tidyverse)
library(rworldmap)
library(sf)
library(leaflet)
library("dplyr")
library(viridisLite)
library(janitor)

  country_data_all <-read_csv("country_all_iso_all.csv", )

countriesLow <- countriesLow %>%
  st_as_sf

#Temporarily removing air date (not sure how to animate/facet/etc. in Leaflet)

country_geom_full<- country_data_all %>%
  left_join(countryExData, by = c("iso3" = "ISO3V10")) %>%
  group_by(iso3) %>%
  mutate(mean_value = mean(value)) %>%
  add_tally(name = "count") %>%
  ungroup() %>%
  select(country, count, mean_value, iso3) %>%
  distinct() %>%
  mutate(iso3 = toupper(iso3)) %>%
  rename(ISO3 = iso3)

country_geom_map_data <- country_geom_full %>%
  mutate(ISO3 = as.factor(ISO3)) %>%
  dplyr::full_join(countriesLow) %>%
  clean_names() %>%
  st_as_sf

pal <- colorNumeric(
  palette = "Greens",
  domain = country_geom_map_data$count)

popup_info<- paste0("<b>Country:</b> ",
                    country_geom_map_data$name, "<br/>",
                    "<b>Population:</b>",
                    country_geom_map_data$pop_est, "<br/>",
                    "<b>Count:</b>",
                    country_geom_map_data$count, "<br/>",
                    "<b>Mean Value:</b> ",
                    round(country_geom_map_data$mean_value))




leaflet(country_geom_map_data) %>%
  addTiles() %>%
  addPolygons(color = ~pal(count), popup = popup_info)

Lessons Learned

• Data Version Control